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Multi-position data collection and dynamic beam sizing: recent improvements to the automatic data-collection algorithms on MASSIF-1

Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better de...

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Detalles Bibliográficos
Autores principales: Svensson, Olof, Gilski, Maciej, Nurizzo, Didier, Bowler, Matthew W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5930350/
https://www.ncbi.nlm.nih.gov/pubmed/29717714
http://dx.doi.org/10.1107/S2059798318003728
Descripción
Sumario:Macromolecular crystallography is now a mature and widely used technique that is essential in the understanding of biology and medicine. Increases in computing power combined with robotics have not only enabled large numbers of samples to be screened and characterized but have also enabled better decisions to be taken on data collection itself. This led to the development of MASSIF-1 at the ESRF, the first beamline in the world to run fully automatically while making intelligent decisions taking user requirements into account. Since opening in late 2014, the beamline has processed over 42 000 samples. Improvements have been made to the speed of the sample-handling robotics and error management within the software routines. The workflows initially put into place, while highly innovative at the time, have been expanded to include increased complexity and additional intelligence using the information gathered during characterization; this includes adapting the beam diameter dynamically to match the diffraction volume within the crystal. Complex multi-position and multi-crystal data collections have now also been integrated into the selection of experiments available. This has led to increased data quality and throughput, allowing even the most challenging samples to be treated automatically.